{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PjSN8gOUIQ1t"
   },
   "source": [
    "\n",
    "# Experiment Manager"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CkMiXmImIhUN",
    "outputId": "c6604c8a-ecd1-4170-d7b6-42bc06ca4977"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "  0     0    0     0    0     0      0      0 --:--:--  0:00:01 --:--:--     0\n",
      "100  869M    0  869M    0     0   9.8M      0 --:--:--  0:01:28 --:--:-- 10.2M\n",
      "Archive:  logs.zip\n",
      "  inflating: logs/exman-train.py/index/000002.yaml  \n",
      "  inflating: logs/exman-train.py/index/000004.yaml  \n",
      "  inflating: logs/exman-train.py/index/000010.yaml  \n",
      "  inflating: logs/exman-train.py/index/000012.yaml  \n",
      "  inflating: logs/exman-train.py/index/000023.yaml  \n",
      "  inflating: logs/exman-train.py/index/000027.yaml  \n",
      "  inflating: logs/exman-train.py/index/000030.yaml  \n",
      "  inflating: logs/exman-train.py/index/000031.yaml  \n",
      "  inflating: logs/exman-train.py/index/000033.yaml  \n",
      "replace logs/exman-train.py/runs/000002/checkpoint.pth.tar? [y]es, [n]o, [A]ll, [N]one, [r]ename: "
     ]
    }
   ],
   "source": [
    "!!pip install diffdist wldhx.yadisk-direct configargparse strconv\n",
    "!curl -L $(yadisk-direct https://yadi.sk/d/GYMBGjXGQr9oFw?w=1) -o logs.zip\n",
    "!unzip logs.zip > unzip.out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "bamF1nUS80W0"
   },
   "outputs": [],
   "source": [
    "!git clone https://github.com/AndrewAtanov/simclr-pytorch.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('./simclr-pytorch')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "90vfPvSRIQ1u"
   },
   "outputs": [],
   "source": [
    "import myexman\n",
    "import pandas as pd\n",
    "\n",
    "index = myexman.Index('./logs/exman-train.py').info().set_index('id')\n",
    "index.root = index.root.apply(lambda x: str(x).replace('/home/aashukha/simclr-pytorch/', ''))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xniSdGb0IQ1u",
    "outputId": "821e111c-a660-4c20-9c96-930251e13e34"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['arch', 'aug', 'augmentation', 'batch_size', 'ckpt', 'config_file',\n",
       "       'data', 'dist', 'dist_address', 'encoder_ckpt', 'eval_freq', 'finetune',\n",
       "       'gpu', 'iters', 'log_freq', 'lr', 'lr_schedule', 'name', 'node_rank',\n",
       "       'number_of_processes', 'opt', 'precompute_emb_bs', 'problem', 'root',\n",
       "       'save_freq', 'scale_lower', 'seed', 'test_bs', 'tmp', 'verbose',\n",
       "       'warmup', 'weight_decay', 'workers', 'world_size', 'time',\n",
       "       'base_lr_linear_scale', 'color_dist_s', 'cooldown', 'cooldown_after',\n",
       "       'momentum', 'multiplier', 'norm_multiplier', 'projection', 'status',\n",
       "       'sync_bn', 'temperature', 'ckpt_iter', 'encode_layer', 'model_id',\n",
       "       'use_all_classes'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 414
    },
    "id": "Ps-fforc-r_N",
    "outputId": "59109585-78d3-45fa-f599-e468b8416b7f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>arch</th>\n",
       "      <th>aug</th>\n",
       "      <th>augmentation</th>\n",
       "      <th>batch_size</th>\n",
       "      <th>ckpt</th>\n",
       "      <th>config_file</th>\n",
       "      <th>data</th>\n",
       "      <th>dist</th>\n",
       "      <th>dist_address</th>\n",
       "      <th>encoder_ckpt</th>\n",
       "      <th>eval_freq</th>\n",
       "      <th>finetune</th>\n",
       "      <th>gpu</th>\n",
       "      <th>iters</th>\n",
       "      <th>log_freq</th>\n",
       "      <th>lr</th>\n",
       "      <th>lr_schedule</th>\n",
       "      <th>name</th>\n",
       "      <th>node_rank</th>\n",
       "      <th>number_of_processes</th>\n",
       "      <th>opt</th>\n",
       "      <th>precompute_emb_bs</th>\n",
       "      <th>problem</th>\n",
       "      <th>root</th>\n",
       "      <th>save_freq</th>\n",
       "      <th>scale_lower</th>\n",
       "      <th>seed</th>\n",
       "      <th>test_bs</th>\n",
       "      <th>tmp</th>\n",
       "      <th>verbose</th>\n",
       "      <th>warmup</th>\n",
       "      <th>weight_decay</th>\n",
       "      <th>workers</th>\n",
       "      <th>world_size</th>\n",
       "      <th>time</th>\n",
       "      <th>base_lr_linear_scale</th>\n",
       "      <th>color_dist_s</th>\n",
       "      <th>cooldown</th>\n",
       "      <th>cooldown_after</th>\n",
       "      <th>momentum</th>\n",
       "      <th>multiplier</th>\n",
       "      <th>norm_multiplier</th>\n",
       "      <th>projection</th>\n",
       "      <th>status</th>\n",
       "      <th>sync_bn</th>\n",
       "      <th>temperature</th>\n",
       "      <th>ckpt_iter</th>\n",
       "      <th>encode_layer</th>\n",
       "      <th>model_id</th>\n",
       "      <th>use_all_classes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ResNet50</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>512</td>\n",
       "      <td></td>\n",
       "      <td>cifar_params.yaml</td>\n",
       "      <td>cifar</td>\n",
       "      <td>ddp</td>\n",
       "      <td>cn-012:8881</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>48000</td>\n",
       "      <td>48</td>\n",
       "      <td>4.0</td>\n",
       "      <td>warmup-anneal</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>lars</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sim-clr</td>\n",
       "      <td>logs/exman-train.py/runs/000002</td>\n",
       "      <td>4800</td>\n",
       "      <td>0.08</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2020-11-18 21:32:53</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>linear</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>MLPv2</td>\n",
       "      <td>fail</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ResNet50</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>128</td>\n",
       "      <td></td>\n",
       "      <td>imagenet_params_epochs200_bs2k.yaml</td>\n",
       "      <td>imagenet</td>\n",
       "      <td>ddp</td>\n",
       "      <td>cn-010:8881</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12510</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>125100</td>\n",
       "      <td>100</td>\n",
       "      <td>2.4</td>\n",
       "      <td>warmup-anneal</td>\n",
       "      <td>imagenet-reproduce</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>lars</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sim-clr</td>\n",
       "      <td>logs/exman-train.py/runs/000004</td>\n",
       "      <td>12510</td>\n",
       "      <td>0.08</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>2020-11-23 16:44:02</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>linear</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>MLPv2</td>\n",
       "      <td>fail</td>\n",
       "      <td>True</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>linear</td>\n",
       "      <td>True</td>\n",
       "      <td>RandomResizedCrop</td>\n",
       "      <td>4096</td>\n",
       "      <td></td>\n",
       "      <td>configs/imagenet_eval_params.yaml</td>\n",
       "      <td>imagenet</td>\n",
       "      <td>dp</td>\n",
       "      <td></td>\n",
       "      <td>/home/aashukha/simclr-pytorch/logs/exman-train...</td>\n",
       "      <td>100</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>28080</td>\n",
       "      <td>1000</td>\n",
       "      <td>1.6</td>\n",
       "      <td>linear</td>\n",
       "      <td>eval_imagenet_newmodels</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>sgd</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>eval</td>\n",
       "      <td>logs/exman-train.py/runs/000010</td>\n",
       "      <td>10000000000000000</td>\n",
       "      <td>0.08</td>\n",
       "      <td>-1</td>\n",
       "      <td>4096.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-11-26 00:44:34</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>linear</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>fail</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>h</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ResNet50</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>128</td>\n",
       "      <td></td>\n",
       "      <td>configs/imagenet_params_epochs600_bs2k.yaml</td>\n",
       "      <td>imagenet</td>\n",
       "      <td>ddp</td>\n",
       "      <td>cn-010:8881</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12510</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>375300</td>\n",
       "      <td>100</td>\n",
       "      <td>2.4</td>\n",
       "      <td>warmup-anneal</td>\n",
       "      <td>imagenet-reproduce</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>lars</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sim-clr</td>\n",
       "      <td>logs/exman-train.py/runs/000012</td>\n",
       "      <td>12510</td>\n",
       "      <td>0.08</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>2020-11-26 01:17:43</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>linear</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>MLPv2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>linear</td>\n",
       "      <td>True</td>\n",
       "      <td>RandomCrop</td>\n",
       "      <td>1024</td>\n",
       "      <td></td>\n",
       "      <td>configs/cifar_eval.yaml</td>\n",
       "      <td>cifar</td>\n",
       "      <td>dp</td>\n",
       "      <td>127.0.0.1:1234</td>\n",
       "      <td>logs/exman-train.py/runs/000002/checkpoint.pth...</td>\n",
       "      <td>1000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>80000</td>\n",
       "      <td>100</td>\n",
       "      <td>0.1</td>\n",
       "      <td>linear</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>sgd</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>eval</td>\n",
       "      <td>logs/exman-train.py/runs/000023</td>\n",
       "      <td>100000000</td>\n",
       "      <td>0.08</td>\n",
       "      <td>-1</td>\n",
       "      <td>1024.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-11-26 16:20:18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        arch   aug       augmentation  ...  encode_layer model_id use_all_classes\n",
       "id                                     ...                                       \n",
       "2   ResNet50  True                NaN  ...           NaN      NaN             NaN\n",
       "4   ResNet50  True                NaN  ...           NaN      NaN             NaN\n",
       "10    linear  True  RandomResizedCrop  ...             h     -1.0           False\n",
       "12  ResNet50  True                NaN  ...           NaN      NaN             NaN\n",
       "23    linear  True         RandomCrop  ...           NaN      NaN             NaN\n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Wy8Mwu_z_EBF",
    "outputId": "a190b8d4-5d30-4c35-ee34-0e8a7da354e2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'arch': 'linear',\n",
       " 'aug': True,\n",
       " 'augmentation': 'RandomResizedCrop',\n",
       " 'base_lr_linear_scale': nan,\n",
       " 'batch_size': 4096,\n",
       " 'ckpt': '',\n",
       " 'ckpt_iter': nan,\n",
       " 'color_dist_s': nan,\n",
       " 'config_file': 'configs/imagenet_eval_params.yaml',\n",
       " 'cooldown': nan,\n",
       " 'cooldown_after': nan,\n",
       " 'data': 'imagenet',\n",
       " 'dist': 'dp',\n",
       " 'dist_address': '',\n",
       " 'encode_layer': nan,\n",
       " 'encoder_ckpt': '/home/aashukha/simclr-pytorch/logs/exman-train.py/runs/000012/checkpoint.pth.tar',\n",
       " 'eval_freq': 100,\n",
       " 'finetune': False,\n",
       " 'gpu': 0,\n",
       " 'iters': 28080,\n",
       " 'log_freq': 1000,\n",
       " 'lr': 1.6,\n",
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       " 'name': 'eval_imagenet_newmodels',\n",
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       " 'norm_multiplier': nan,\n",
       " 'number_of_processes': 1,\n",
       " 'opt': 'sgd',\n",
       " 'precompute_emb_bs': -1.0,\n",
       " 'problem': 'eval',\n",
       " 'projection': nan,\n",
       " 'root': 'logs/exman-train.py/runs/000033',\n",
       " 'save_freq': 10000000000000000,\n",
       " 'scale_lower': 0.08,\n",
       " 'seed': -1,\n",
       " 'status': nan,\n",
       " 'sync_bn': nan,\n",
       " 'temperature': nan,\n",
       " 'test_bs': 4096.0,\n",
       " 'time': Timestamp('2020-12-05 14:49:17'),\n",
       " 'tmp': False,\n",
       " 'use_all_classes': nan,\n",
       " 'verbose': False,\n",
       " 'warmup': 0.0,\n",
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       " 'workers': 20,\n",
       " 'world_size': 1}"
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     "execution_count": 39,
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   "source": [
    "dict(index.loc[33])"
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   "outputs": [
    {
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       "         t  test_loss  test_acc  ...        lr   data_time      it_time\n",
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    "logs = pd.read_csv(index.loc[33].root + '/logs.csv')\n",
    "logs.tail(2)"
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   "execution_count": null,
   "metadata": {
    "id": "w-QviLaJ9P-u"
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